| 1. | Ir guiding technology based on multispectral imaging for air to air missile
红外成像制导及对抗技术 |
| 2. | Fusion of remote sensing images - the fusion of spot panchromatic and multispectral images
全色波段和多光谱影像的融合 |
| 3. | With the development of multispectral remote - sensing technology , compression of multispectral images is of more and more importance
随着多光谱遥感技术的发展，多光谱图像的压缩受到越来越多的关注。 |
| 4. | First , the panchromatic image and the intensity component of multispectral image are decomposed based on the discrete biorthogonal wavelet transform at first level
首先,对全色图像和由ihs变换得到的多谱图像亮度成分进行单层双正交小波分解。 |
| 5. | With the expanding scope of the application fields of remote sensing in recent years , multispectral images cannot meet the increasing demand for scientific researches
近年来,随着遥感技术应用领域的不断扩大,以往的多光谱图像数据已经不能满足人们日益增长的科研和生产需求。 |
| 6. | Though double parallel feedforward neural networks ( dpfnn ) has been successfully used to classify the multispectral images , the generalization performance of dpfnn has not been studied until now
双并联前向神经网络（ doubleparallelfeedfo 。 rdneuralnetworks一dpfnn ）已成功应用于多光谱数据分类，对其推广性的研究具有十分重要的意义。 |
| 7. | In the end , multispectral image fusion algorithms based on principal component analysis and clustering algorithms are introduced . multispectral image fusion based on unsupervised classification is realized
最后，还介绍了基于主成分分析( pca )的多光谱图象融合方法以及非监督分类的方法，并实现了基于非监督分类的多光谱图象融合算法。 |
| 8. | In this paper , some theories and methods of compression of multispectral images are reviewed and researches on multispectral remote - sensing image compression by karhunen - loeve transformation ( klt ) and neural network are discussed
本文在对一些目前已有的多光谱图像压缩方法进行深入研究的基础上，分别利用k - l变换和神经网络的方法实现了遥感图像的压缩。 |
| 9. | The dependences in multitemporal multispectral images by independent component analysis are reduced . in the algorithm , damped factor is imported to reduce the dependence on initial weights , thus the robust of the algorithm is improved
在改进的独立成分学习算法中,通过在梯度下降方法中引入阻尼因子,降低了对初始值的依赖,提高了独立成分求解的稳健性。 |
| 10. | It can not only enhances the spatial detail features of result image greatly , but also preserves more spectral characteristics from original multispectral images , and fulfills the unification of spatial details enhancement and spectral information preservation effectively
它既能较好地增强融合结果图像的空间细节特征,又能保持较多的原始多谱图像的光谱特性,实现了空间细节增强和光谱信息保留的有效统一。 |
